1995 Volume 7 Issue 5 Pages 978-987
The genetic algorithm (GA) is one of basic models of evolution and is one of the effective tools for constructing evalvable/adaptive complex systems. There are two distinct approaches to genetic based machine learning(GBML). These two approaches are called the Michigan approach and the Pitt approach. The Michigan approach uses a single set of production rules or classifiers. This approach needs an apportionment of credit system to adjust strength of the rules in proportion to the amount of payoffs from the task environment as well as to the contribution of the rules to the goal. The individual in the Pitt approach, on the other hand, comprises a set of rules. Each set of rules is used in the production system and the payoffs from the environment are directly given to the set of rules. The Pitt approach does not need the apportionment of credit system. However, through some experiments using the Pitt approach, it has been found that the improvement of individual rules of a chromosome is hard to achieve.This paper presents a genetic algorithm with a local improvement mechnism and the new method is applied to the machine learning. The new algorithm is efficient in improving the local portions of the chromsomes. An obstacle avoidance of mobile robot is simulated using the new method and fuzzy control rules are found.
